Subject description - BE2M31AEDA
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Explanatory Notes
Instructions
Anotation:
V rámci předmětu Analýza experimentálních dat si studenti ověří aplikace základních DSP metod na různých úlohách a rovněž budou aplikovat základní statistické a klasifikační metody pro vyhodnocení a interpretaci dat. V rámci semestrální práce budou studenti zpracovávat a vyhodnocovat reálná data, a na závěr prezentovat výsledky jejich práce. Cílem předmětu je naučit studenty kriticky myslet a získat dovedností při samostatném řešení praktických úkolů.
Course outlines:
1. | | Introduction to the subject "Experimental Data Analysis", introduction to data |
2. | | Introduction to the statistics, probability distributions, and plotting statistical data |
3. | | Hypothesis testing, group differences, paired test, effect size |
4. | | Correlations, normality of data testing, parametric vs. non-parametric tests |
5. | | Analysis of variance, post-hoc testing |
6. | | Type I & Type II errors, multiple comparisons, sample size estimation |
7. | | Factorial analysis of variance |
8. | | Introduction to models, regression analysis |
9. | | Supervised classification |
10. | | Model validation |
11. | | Unsupervised classification |
12. | | Dimensionality reduction, data interpretation |
13. | | Reserve, consultation of semestral projects |
14. | | Presentation of obtained results |
Exercises outline:
1. | | Introduction to Matlab |
2. | | Introduction to the statistics, probability distributions, and plotting statistical data |
3. | | Hypothesis testing, group differences, paired test, effect size |
4. | | Correlations, normality of data testing, parametric vs. non-parametric tests |
5. | | Analysis of variance, post-hoc testing |
6. | | Type I & Type II errors, multiple comparisons, sample size estimation |
7. | | Factorial analysis of variance |
8. | | Introduction to models, regression analysis |
9. | | Supervised classification |
10. | | Model validation |
11. | | Unsupervised classification |
12. | | Dimensionality reduction, data interpretation |
13. | | Reserve, consultation of semestral projects |
14. | | Presentation of obtained results |
Literature:
[1] | | Vidakovic B. Statistics for bioengineering sciences: with Matlab and WinBUGS support. New Yourk: Springer, 2011. |
[2] | | Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning : data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer, 2001. |
Requirements:
Subject is included into these academic programs:
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Page updated 26.4.2024 17:55:06, semester: Z,L/2023-4, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs |
Proposal and Realization: I. Halaška (K336), J. Novák (K336) |